Data Science Blog Post¶

Comparison of the most popular programming languages accross USA, Europe and India¶

In this project, I am exploring the Stack Overflow data from 2017 to better understand:

Which among the USA & Canada, Europe and India is a better place to pursue interest in the field of computer science as a professional and also pursue higher studies as a student.
The salary differences among these regions.
The most popular programming languages across these regions.
The most demanded programming languages across these regions.

import numpy as np
import pandas as pd 
from collections import Counter
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings("ignore")

pd.pandas.set_option('display.max_columns', None) #display all columns 
pd.pandas.set_option('display.max_rows', None)#display all rows

#read in the data
df_2017 = pd.read_csv('../data/2017_stack.csv')

df_2017.head()

def region(df):
    '''
    Returns a dataframe with regions seperate into USA & Canada, Europe and India.
    
    Parameters:
    df: a raw data dataframe
    
    Returns:
    df: a dataframe with a new column region_specified
    
    '''
    
    usa_canada = ['United States', 'Canada']
    europe = ['Liechtenstein', 'Switzerland', 
           'Iceland', 'Norway','Denmark', 
           'Ireland','United Kingdom', 'Germany', 
           'Netherlands', 'Sweden', 'Luxembourg', 'Austria', 
           'Finland', 'France', 'Belgium', 'Spain', 'Italy',
           'Poland','Portugal']

    india = ['India']
    
    #Add a new catagory seperating to western and eastern
    df['region_specified'] = df['Country'].apply(lambda x: 'usa_canada' 
                                                 if x in usa_canada else ('europe' if x in europe 
                                                                          else ('india' if x in india 
                                                                                else 'other')))
    
    return df

def create_df(df):
    '''
    Return a df with only the necessary columns.
    
    Parameters:
    df: a raw data dataframe
    
    Returns:
    queries_df: a filtered dataframe with only useful columns
    
    '''
    
    select_columns = ['Country', 'YearsCodedJob', 'EmploymentStatus','Salary','HaveWorkedLanguage', 'WantWorkLanguage' ,'region_specified']
    queries_df = pd.DataFrame(df.query("Professional == 'Professional developer'and \
                                       EmploymentStatus == 'Employed full-time' and (region_specified == 'europe' or region_specified == 'india' or region_specified == 'usa_canada')"))[select_columns]
    return queries_df

df_2017 = region(df_2017)
df_2017_new = create_df(df_2017)
df_2017_new

#Compare selected indicators between the regions
compiled_df = df_2017_new.groupby(['region_specified','YearsCodedJob']).mean()
compiled_df

def decode_yearscoded(df):
    """
    Return a column with a number to specify years coded in a job year.
    
    Parameters:
    df: an input dataframe
    
    Returns:
    dataframe: a converted dataframe with YearsCodedJob column becomes a representable quantity
    
    """
    mapping_years = {'1 to 2 years' : 1, 
                '10 to 11 years' : 10, 
                '11 to 12 years' : 11, 
                '12 to 13 years' : 12,
                '13 to 14 years' : 13, 
                '14 to 15 years' : 14, 
                '15 to 16 years' : 15, 
                '16 to 17 years' : 16,
                '17 to 18 years' : 17, 
                '18 to 19 years' : 18, 
                '19 to 20 years' : 19, 
                '2 to 3 years' : 2,
                '20 or more years' : 20, 
                '3 to 4 years' : 3, 
                '4 to 5 years' : 4, 
                '5 to 6 years' : 5, 
                '6 to 7 years' : 6, 
                '7 to 8 years' : 7, 
                '8 to 9 years' : 8, 
                '9 to 10 years' : 9, 
                'Less than a year' : 0}
    
    df_decoded = df.reset_index()
    df_decoded['YearsCodedJob'] = df_decoded['YearsCodedJob'].apply(lambda x: mapping_years[x])
    df_decoded['YearsCodedJob'] = pd.to_numeric(df_decoded['YearsCodedJob'])
    
    return df_decoded

compiled = decode_yearscoded(compiled_df)
compiled = compiled.sort_values(by = ['region_specified','YearsCodedJob'])
#compiled.groupby('region_specified')
compiled.set_index('YearsCodedJob', inplace=True)

compiled.groupby('region_specified')['Salary']
compiled['Salary'].fillna(method = 'backfill',inplace = True)
compiled

lang_df = df_2017_new[['HaveWorkedLanguage', 'WantWorkLanguage','region_specified']]

lang_df_usa = lang_df[lang_df['region_specified'] == 'usa_canada']

lang_df_europe = lang_df[lang_df['region_specified'] == 'europe']

lang_df_india = lang_df[lang_df['region_specified'] == 'india']

lang_df.groupby(['region_specified'])

<pandas.core.groupby.generic.DataFrameGroupBy object at 0x1216eaba8>

def split_cols(df, column):
    ''' Split column by ;,
        returns a splited series.
    '''
    df = df.dropna(subset=[column])
    column_series = df[column].apply(lambda x: x.split(';'))
    
    return column_series

lang_worked = split_cols(lang_df, 'HaveWorkedLanguage')
lang_demand = split_cols(lang_df, 'WantWorkLanguage')

lang_worked_usa = split_cols(lang_df_usa, 'HaveWorkedLanguage')
lang_demand_usa = split_cols(lang_df_usa, 'WantWorkLanguage')

lang_worked_europe = split_cols(lang_df_europe, 'HaveWorkedLanguage')
lang_demand_europe = split_cols(lang_df_europe, 'WantWorkLanguage')

lang_worked_india = split_cols(lang_df_india, 'HaveWorkedLanguage')
lang_demand_india = split_cols(lang_df_india, 'WantWorkLanguage')

def flatten(array_list):
    ''' 
        returns a flattened list of a nested list.
    '''
    
    objects = []
    
    for row in array_list:
        for obj in row:
            objects.append(obj.strip())
            
    return objects

lang_worked_list = flatten(lang_worked)
lang_demand_list = flatten(lang_demand)

lang_worked_list_usa = flatten(lang_worked_usa)
lang_demand_list_usa = flatten(lang_demand_usa)

lang_worked_list_europe = flatten(lang_worked_europe)
lang_demand_list_europe = flatten(lang_demand_europe)

lang_worked_list_india = flatten(lang_worked_india)
lang_demand_list_india = flatten(lang_demand_india)

def group_list(data_list,region_specified = 'all'):
    ''' Group by count to a list,
        returns a result dict.
    '''
    
    grouped_list = dict(Counter(data_list))
    grouped_dict = [{'Language':key, 'Count': value,'region':region_specified} for key, value in grouped_list.items()]
    
    return grouped_dict
lang_worked_dict = group_list(lang_worked_list)
lang_demand_dict = group_list(lang_demand_list)

lang_worked_dict_usa = group_list(lang_worked_list_usa,'usa_canada')
lang_demand_dict_usa = group_list(lang_demand_list_usa,'usa_canada')

lang_worked_dict_europe = group_list(lang_worked_list_europe,'europe')
lang_demand_dict_europe = group_list(lang_demand_list_europe,'europe')

lang_worked_dict_india = group_list(lang_worked_list_india,'india')
lang_demand_dict_india = group_list(lang_demand_list_india,'india')

def append_df(dicts):
    df1 = pd.DataFrame(dicts[0])
    df2 = pd.DataFrame(dicts[1])
    df3 = pd.DataFrame(dicts[2])
    
    df = df1.append(df2.append(df3))
    
    return df

worked_languages = append_df([lang_worked_dict_usa, lang_worked_dict_europe,lang_worked_dict_india])
demand_languages = append_df([lang_demand_dict_usa, lang_demand_dict_europe,lang_demand_dict_india])


df1 = pd.DataFrame(lang_worked_dict)
total_worked = df1.Count.sum()
df1['Percentage'] = df1['Count'].apply(lambda x : (x / total_worked) * 100)
df1 = df1.sort_values(by = 'Percentage',ascending = False).head(10)

df2 = pd.DataFrame(lang_demand_dict)
total_worked = df2.Count.sum()
df2['Percentage'] = df2['Count'].apply(lambda x : (x / total_worked) * 100)
df2 = df2.sort_values(by = 'Percentage',ascending = False).head(10)

def calc_percentage(df,column):
    df_copy = df
    series = []
    
    for val in df_copy[column].unique():
        series.append(df_copy[df_copy[column] == val]['Count'] / 
                      df_copy[df_copy[column] == val]['Count'].sum())
    
    joined_series = pd.Series()
    for i_series in series:
        joined_series = joined_series.append(i_series)
        
    return joined_series

worked_languages['Percentage'] = calc_percentage(worked_languages, 'region')
demand_languages['Percentage'] = calc_percentage(demand_languages, 'region')

top_worked = worked_languages.sort_values(by=['Percentage'], ascending=False).head(20)['Language'].unique()
top_demanded = demand_languages.sort_values(by=['Percentage'], ascending=False).head(20)['Language'].unique()

worked_lang_chart = worked_languages[worked_languages['Language'].isin(top_worked)]
demand_lang_chart = demand_languages[demand_languages['Language'].isin(top_demanded)]

Results¶

Which among the USA & Canada, Europe and India is a better place to pursue interest in the field of computer science as a professional and also pursue higher studies as a student.¶

plt.figure(figsize=(20,8))
compiled.groupby('region_specified')['Salary'].plot(legend = True)
plt.title("Salary Comparison between USA & Canada, Europe and India",fontsize = 20);
plt.xlabel('Years Coded in a Job',fontsize = 15)
plt.ylabel('Average Salary',fontsize = 15)

Text(0, 0.5, 'Average Salary')

The salary differences among these regions.¶

compiled.groupby('region_specified').mean().Salary

region_specified
europe        55003.876123
india         18459.066039
usa_canada    97094.096776
Name: Salary, dtype: float64

plt.figure(figsize=(20,8))
compiled.groupby('region_specified').mean().Salary.plot(kind = 'bar')
plt.title('Average salary across the USA, Canada, Europe and India',fontsize = 25)
plt.xlabel('Region',fontsize = 15)
plt.ylabel('Average Salary(USD)',fontsize = 15)

Text(0, 0.5, 'Average Salary(USD)')

The most popular programming languages across these regions.¶

plt.figure(figsize=(20,8))
sns.barplot(x = 'Percentage', 
            y = 'Language',
            hue = 'region',
            data = worked_lang_chart.sort_values(by='Percentage', ascending=False))

plt.xlabel("Percentage Usage", fontsize = 14)
plt.ylabel("Programming Languages", fontsize = 14)
plt.legend(fontsize='x-large', title_fontsize='40')
plt.title('Top most popular Programming Languages used across the USA, Canada, Europe and India', size = 16)
plt.show()

The most demanded programming languages across these regions.¶

plt.figure(figsize=(20,8))
sns.barplot(x = 'Percentage', 
            y = 'Language',
            hue = 'region',
            data = demand_lang_chart.sort_values(by='Percentage', ascending=False))

plt.title('Top most popular Programming Languages in demand across the USA, Canada, Europe and India', size = 16)
plt.xlabel("Percentage Usage", fontsize = 14)
plt.ylabel("Programming Languages", fontsize = 14)
plt.legend(fontsize='x-large', title_fontsize='40')
plt.show()

	Respondent	Professional	ProgramHobby	Country	University	EmploymentStatus	FormalEducation	MajorUndergrad	HomeRemote	CompanySize	CompanyType	YearsProgram	YearsCodedJob	YearsCodedJobPast	DeveloperType	WebDeveloperType	MobileDeveloperType	NonDeveloperType	CareerSatisfaction	JobSatisfaction	ExCoderReturn	ExCoderNotForMe	ExCoderBalance	ExCoder10Years	ExCoderBelonged	ExCoderSkills	ExCoderWillNotCode	ExCoderActive	PronounceGIF	ProblemSolving	BuildingThings	LearningNewTech	BoringDetails	JobSecurity	DiversityImportant	AnnoyingUI	FriendsDevelopers	RightWrongWay	UnderstandComputers	SeriousWork	InvestTimeTools	WorkPayCare	KinshipDevelopers	ChallengeMyself	CompetePeers	ChangeWorld	JobSeekingStatus	HoursPerWeek	LastNewJob	AssessJobIndustry	AssessJobRole	AssessJobExp	AssessJobDept	AssessJobTech	AssessJobProjects	AssessJobCompensation	AssessJobOffice	AssessJobCommute	AssessJobRemote	AssessJobLeaders	AssessJobProfDevel	AssessJobDiversity	AssessJobProduct	AssessJobFinances	ImportantBenefits	ClickyKeys	JobProfile	ResumePrompted	LearnedHiring	ImportantHiringAlgorithms	ImportantHiringTechExp	ImportantHiringCommunication	ImportantHiringOpenSource	ImportantHiringPMExp	ImportantHiringCompanies	ImportantHiringTitles	ImportantHiringEducation	ImportantHiringRep	ImportantHiringGettingThingsDone	Currency	Overpaid	TabsSpaces	EducationImportant	EducationTypes	SelfTaughtTypes	TimeAfterBootcamp	CousinEducation	WorkStart	HaveWorkedLanguage	WantWorkLanguage	HaveWorkedFramework	WantWorkFramework	HaveWorkedDatabase	WantWorkDatabase	HaveWorkedPlatform	WantWorkPlatform	IDE	AuditoryEnvironment	Methodology	VersionControl	CheckInCode	ShipIt	OtherPeoplesCode	ProjectManagement	EnjoyDebugging	InTheZone	DifficultCommunication	CollaborateRemote	MetricAssess	EquipmentSatisfiedMonitors	EquipmentSatisfiedCPU	EquipmentSatisfiedRAM	EquipmentSatisfiedStorage	EquipmentSatisfiedRW	InfluenceInternet	InfluenceWorkstation	InfluenceHardware	InfluenceServers	InfluenceTechStack	InfluenceDeptTech	InfluenceVizTools	InfluenceDatabase	InfluenceCloud	InfluenceConsultants	InfluenceRecruitment	InfluenceCommunication	StackOverflowDescribes	StackOverflowSatisfaction	StackOverflowDevices	StackOverflowFoundAnswer	StackOverflowCopiedCode	StackOverflowJobListing	StackOverflowCompanyPage	StackOverflowJobSearch	StackOverflowNewQuestion	StackOverflowAnswer	StackOverflowMetaChat	StackOverflowAdsRelevant	StackOverflowAdsDistracting	StackOverflowModeration	StackOverflowCommunity	StackOverflowHelpful	StackOverflowBetter	StackOverflowWhatDo	StackOverflowMakeMoney	Gender	HighestEducationParents	Race	SurveyLong	QuestionsInteresting	QuestionsConfusing	InterestedAnswers	Salary	ExpectedSalary
0	1	Student	Yes, both	United States	No	Not employed, and not looking for work	Secondary school	NaN	NaN	NaN	NaN	2 to 3 years	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	With a soft "g," like "jiff"	Strongly agree	Strongly agree	Agree	Disagree	Strongly agree	Agree	Agree	Disagree	Somewhat agree	Disagree	Strongly agree	Strongly agree	Strongly disagree	Agree	Agree	Disagree	Agree	I'm not actively looking, but I am open to new...	0.0	Not applicable/ never	Very important	Very important	Important	Very important	Very important	Very important	Important	Very important	Very important	Very important	Very important	Very important	Somewhat important	Not very important	Somewhat important	Stock options; Vacation/days off; Remote options	Yes	Other	NaN	NaN	Important	Important	Important	Somewhat important	Important	Not very important	Not very important	Not at all important	Somewhat important	Very important	NaN	NaN	Tabs	NaN	Online course; Open source contributions	NaN	NaN	NaN	6:00 AM	Swift	Swift	NaN	NaN	NaN	NaN	iOS	iOS	Atom; Xcode	Turn on some music	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Somewhat satisfied	Not very satisfied	Not at all satisfied	Very satisfied	Satisfied	Not very satisfied	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	I have created a CV or Developer Story on Stac...	9.0	Desktop; iOS app	At least once each week	Haven't done at all	Once or twice	Haven't done at all	Haven't done at all	Several times	Several times	Once or twice	Somewhat agree	Strongly disagree	Strongly disagree	Strongly agree	Agree	Strongly agree	Strongly agree	Strongly disagree	Male	High school	White or of European descent	Strongly disagree	Strongly agree	Disagree	Strongly agree	NaN	NaN
1	2	Student	Yes, both	United Kingdom	Yes, full-time	Employed part-time	Some college/university study without earning ...	Computer science or software engineering	More than half, but not all, the time	20 to 99 employees	Privately-held limited company, not in startup...	9 to 10 years	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	With a hard "g," like "gift"	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	No	Other	NaN	Some other way	Important	Important	Important	Important	Somewhat important	Somewhat important	Not very important	Somewhat important	Not very important	Very important	British pounds sterling (£)	NaN	Spaces	NaN	Online course; Self-taught; Hackathon; Open so...	Official documentation; Stack Overflow Q&A; Other	NaN	NaN	10:00 AM	JavaScript; Python; Ruby; SQL	Java; Python; Ruby; SQL	.NET Core	.NET Core	MySQL; SQLite	MySQL; SQLite	Amazon Web Services (AWS)	Linux Desktop; Raspberry Pi; Amazon Web Servic...	Atom; Notepad++; Vim; PyCharm; RubyMine; Visua...	Put on some ambient sounds (e.g. whale songs, ...	NaN	Git	Multiple times a day	Agree	Disagree	Strongly disagree	Agree	Somewhat agree	Disagree	Strongly disagree	Customer satisfaction; On time/in budget; Peer...	Not very satisfied	Satisfied	Satisfied	Satisfied	Somewhat satisfied	Satisfied	No influence at all	No influence at all	No influence at all	No influence at all	No influence at all	No influence at all	No influence at all	No influence at all	No influence at all	No influence at all	No influence at all	I have created a CV or Developer Story on Stac...	8.0	Desktop; iOS browser; iOS app; Android browser...	Several times	Several times	Once or twice	Once or twice	Once or twice	Haven't done at all	Several times	At least once each week	Disagree	Strongly disagree	Strongly disagree	Strongly agree	Agree	Strongly agree	Strongly agree	Strongly disagree	Male	A master's degree	White or of European descent	Somewhat agree	Somewhat agree	Disagree	Strongly agree	NaN	37500.0
2	3	Professional developer	Yes, both	United Kingdom	No	Employed full-time	Bachelor's degree	Computer science or software engineering	Less than half the time, but at least one day ...	10,000 or more employees	Publicly-traded corporation	20 or more years	20 or more years	NaN	Other	NaN	NaN	NaN	8.0	9.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	With a hard "g," like "gift"	Strongly agree	Strongly agree	Strongly agree	Somewhat agree	Agree	Strongly agree	Agree	Somewhat agree	Disagree	Disagree	Agree	Somewhat agree	Disagree	Somewhat agree	Agree	Disagree	Agree	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Yes	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	British pounds sterling (£)	Neither underpaid nor overpaid	Spaces	Not very important	Self-taught; Coding competition; Hackathon; Op...	Official documentation; Trade book; Textbook; ...	NaN	NaN	9:00 AM	Java; PHP; Python	C; Python; Rust	NaN	NaN	MySQL	NaN	NaN	NaN	Sublime Text; Vim; IntelliJ	Turn on some music	Agile; Lean; Scrum; Extreme; Pair; Kanban	Mercurial	Multiple times a day	Agree	Disagree	Disagree	Agree	Agree	Disagree	Somewhat agree	Customer satisfaction; Benchmarked product per...	Very satisfied	Somewhat satisfied	Satisfied	Satisfied	Somewhat satisfied	Very satisfied	A lot of influence	Some influence	Some influence	Some influence	A lot of influence	Some influence	Some influence	Some influence	Some influence	Some influence	Some influence	I have created a CV or Developer Story on Stac...	8.0	Desktop; iOS browser; iOS app	Once or twice	Haven't done at all	Haven't done at all	Haven't done at all	Haven't done at all	Haven't done at all	At least once each day	At least once each day	Disagree	Disagree	Strongly disagree	Strongly agree	Agree	Agree	Agree	Disagree	Male	A professional degree	White or of European descent	Somewhat agree	Agree	Disagree	Agree	113750.0	NaN
3	4	Professional non-developer who sometimes write...	Yes, both	United States	No	Employed full-time	Doctoral degree	A non-computer-focused engineering discipline	Less than half the time, but at least one day ...	10,000 or more employees	Non-profit/non-governmental organization or pr...	14 to 15 years	9 to 10 years	NaN	NaN	NaN	NaN	Data scientist	6.0	3.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	With a soft "g," like "jiff"	Strongly agree	Strongly agree	Strongly agree	Disagree	Somewhat agree	Agree	Agree	Agree	Somewhat agree	Strongly disagree	Strongly agree	Agree	Disagree	Strongly agree	Strongly agree	Somewhat agree	Agree	I am actively looking for a job	5.0	Between 2 and 4 years ago	Somewhat important	Somewhat important	Somewhat important	Important	Important	Very important	Important	Very important	Important	Somewhat important	Not very important	Very important	Important	Very important	Very important	Stock options; Annual bonus; Health benefits; ...	Yes	LinkedIn; Other	NaN	A friend, family member, or former colleague t...	Somewhat important	Somewhat important	Very important	Very important	Somewhat important	Somewhat important	Not very important	Not very important	Important	Very important	NaN	NaN	Spaces	NaN	NaN	NaN	NaN	NaN	9:00 AM	Matlab; Python; R; SQL	Matlab; Python; R; SQL	React	Hadoop; Node.js; React	MongoDB; Redis; SQL Server; MySQL; SQLite	MongoDB; Redis; SQL Server; MySQL; SQLite	Windows Desktop; Linux Desktop; Mac OS; Amazon...	Windows Desktop; Linux Desktop; Mac OS; Amazon...	Notepad++; Sublime Text; TextMate; Vim; IPytho...	Turn on some music	Agile	Git	Multiple times a day	Somewhat agree	Agree	Somewhat agree	Somewhat agree	Strongly agree	Disagree	Somewhat agree	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	I have created a CV or Developer Story on Stac...	10.0	Desktop; iOS browser; iOS app	At least once each week	Several times	At least once each week	Several times	At least once each week	Several times	At least once each day	At least once each day	Agree	Strongly disagree	Strongly disagree	Strongly agree	Strongly agree	Agree	Strongly agree	Disagree	Male	A doctoral degree	White or of European descent	Agree	Agree	Somewhat agree	Strongly agree	NaN	NaN
4	5	Professional developer	Yes, I program as a hobby	Switzerland	No	Employed full-time	Master's degree	Computer science or software engineering	Never	10 to 19 employees	Privately-held limited company, not in startup...	20 or more years	10 to 11 years	NaN	Mobile developer; Graphics programming; Deskto...	NaN	NaN	NaN	6.0	8.0	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	With a soft "g," like "jiff"	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	Satisfied	Satisfied	Satisfied	Satisfied	Satisfied	Satisfied	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN

		Salary
region_specified	YearsCodedJob
europe	1 to 2 years	32655.926063
	10 to 11 years	55005.604004
	11 to 12 years	56433.646110
	12 to 13 years	66491.920379
	13 to 14 years	58076.597771
	14 to 15 years	61083.505846
	15 to 16 years	66574.505246
	16 to 17 years	56545.244213
	17 to 18 years	74607.726416
	18 to 19 years	69917.056718
	19 to 20 years	65585.414297
	2 to 3 years	35393.722670
	20 or more years	71548.258235
	3 to 4 years	41565.629970
	4 to 5 years	43922.979244
	5 to 6 years	48044.298812
	6 to 7 years	51243.057088
	7 to 8 years	53066.936016
	8 to 9 years	58143.840109
	9 to 10 years	57841.987086
	Less than a year	31333.542293
india	1 to 2 years	6075.597550
	10 to 11 years	22289.985657
	11 to 12 years	23015.605009
	12 to 13 years	36558.508295
	13 to 14 years	41097.734058
	14 to 15 years	24714.921940
	15 to 16 years	37072.382910
	16 to 17 years	15693.500710
	17 to 18 years	NaN
	18 to 19 years	13213.918661
	19 to 20 years	16150.345030
	2 to 3 years	6984.236021
	20 or more years	11863.162531
	3 to 4 years	10160.622686
	4 to 5 years	11365.459756
	5 to 6 years	11534.464699
	6 to 7 years	13027.659841
	7 to 8 years	26245.865407
	8 to 9 years	18514.617621
	9 to 10 years	21089.833672
	Less than a year	7758.046109
usa_canada	1 to 2 years	66048.231652
	10 to 11 years	104600.165825
	11 to 12 years	101134.957632
	12 to 13 years	114439.264463
	13 to 14 years	105899.095024
	14 to 15 years	111082.076019
	15 to 16 years	105973.495969
	16 to 17 years	108566.036697
	17 to 18 years	114903.703186
	18 to 19 years	112476.262784
	19 to 20 years	112985.756629
	2 to 3 years	70528.317235
	20 or more years	119795.904932
	3 to 4 years	76658.906411
	4 to 5 years	83786.303870
	5 to 6 years	86507.483835
	6 to 7 years	92715.948341
	7 to 8 years	92382.892349
	8 to 9 years	99294.652430
	9 to 10 years	96165.524566
	Less than a year	63031.052448